Conclave Simulation

A Monte Carlo simulation engine for modeling papal conclaves and forecasting outcomes based on cardinal elector dynamics, voting rules, and voting behavior models.

Background

This project simulates the process of a papal conclave, where 135 cardinal electors gather to elect a new Pope through a series of voting rounds. The model incorporates ideological preferences, regional affinity, papabile status, bandwagon effects, and vote stickiness to create realistic voting dynamics.

Installation

# Clone the repository
git clone https://github.com/potalora/conclave_sim.git
cd conclave_sim

# Create and activate a virtual environment
python -m venv venv
source venv/bin/activate  # On Windows: venv\Scripts\activate

# Install dependencies
pip install -r requirements.txt

Usage

Running Simulations

To run a simulation with default parameters:

python -m src.simulate

To run a simulation with optimized parameters for faster convergence:

python -m src.simulate \
--num-simulations 100 \
--max-rounds 20 \
--enable-dynamic-beta \
--beta-increment-amount 0.3 \
--beta-increment-interval-rounds 1 \
--initial-beta-weight 1.5 \
--bandwagon-strength 0.7 \
--papabile-weight-factor 2.0 \
--stickiness-factor 0.8 \
--supermajority-threshold 0.56

Generating Visualizations

After running simulations, you can generate visualizations using:

python generate_visualizations.py

Visualization outputs will be saved to the data/plots directory.

Recent Simulation Results

Optimized Parameters (May 2025)

Our most recent simulation run with 1000 iterations achieved significantly improved convergence rates using the following parameters:

Parameter	Value	Description
initial_beta_weight	1.5	Controls sensitivity to ideological distance
beta_increment_amount	0.3	Increases beta weight per round to promote convergence
beta_increment_interval_rounds	1	How often beta increments
bandwagon_strength	0.7	Strength of tendency to vote for front-runners
papabile_weight_factor	2.0	Multiplicative advantage for "papabile" cardinals
stickiness_factor	0.8	Tendency of electors to maintain previous votes
supermajority_threshold	0.56	Required threshold to elect a pope (56%)

Key Results

• Success Rate: 89% of simulations reached a decisive outcome
• Average Rounds: 10.5 rounds for successful simulations
• Convergence Range: 6-20 rounds

Visualizations of these results can be found in the data/plots/1000_fast directory after running the visualization script.

Project Structure

/ (root)
├─ data/                  # Raw and processed elector CSVs, simulation results
│  ├─ plots/              # Generated visualizations
├─ src/                   # Core simulation modules
│  ├─ ingest.py           # Elector roster loader
│  ├─ model.py            # Preference & transition models
│  ├─ simulate.py         # Monte Carlo engine
│  └─ viz.py              # Plotting and reporting
├─ tests/                 # Pytest suites and test data fixtures
├─ docs/                  # Markdown documentation
└─ README.md              # Project overview

Model Parameters

The simulation model supports various configurable parameters:

Parameter	Description
initial_beta_weight	Controls sensitivity to ideological distance
beta_increment_amount	Increases beta weight per round to promote convergence
bandwagon_strength	Strength of tendency to vote for front-runners
papabile_weight_factor	Multiplicative advantage for "papabile" cardinals
stickiness_factor	Tendency of electors to maintain previous votes
regional_bonus	Bonus for candidates from the same region
supermajority_threshold	Required threshold to elect a pope

Features

• Multi-round papal election simulation with configurable parameters
• Ideological distance-based preference modeling
• Regional affinity effects
• Papabile status weighting
• Bandwagon effects and vote stickiness
• Comprehensive visualization tools
• Parallelized simulation for performance

Visualizations

The visualization module (src/viz.py) provides several visualizations for simulation results:

• Rounds Distribution: Histogram showing how many rounds it takes for simulations to converge
• Winner Distribution: Bar chart showing which electors won and how frequently
• Convergence Timeline: Visual mapping of when each simulation reached consensus
• Parameter Impact Matrix: Heatmap comparing parameter sets and their effects

Contributing

Contributions to the Conclave Simulation project are welcome. Please follow these steps to contribute:

1. Fork the repository
2. Create a feature branch (git checkout -b feature/my-feature)
3. Commit your changes (git commit -am 'Add new feature')
4. Push to the branch (git push origin feature/my-feature)
5. Create a new Pull Request

Please ensure your code follows the project's coding standards outlined in the repository documentation.

License

This project is licensed under the MIT License - see the LICENSE file for details.